Next Article in Journal
Nesting Patterns of Loggerhead Sea Turtles (Caretta caretta): Development of a Multiple Regression Model Tested in North Carolina, USA
Previous Article in Journal
Place and City: Toward Urban Intelligence
Open AccessArticle

Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19967-15433, Iran
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(9), 347;
Received: 27 June 2018 / Revised: 1 August 2018 / Accepted: 20 August 2018 / Published: 24 August 2018
PDF [5977 KB, uploaded 24 August 2018]


The use of local information for the classification and modelling of spatial variables has increased with the application of statistical and machine learning algorithms, such as support vector machines (SVMs). This study presents a new local SVM (LSVM) model that was developed to model the probability of urban development and simulate urban growth in a subregion in the southwestern suburb of the Tehran metropolitan area, Iran, for the periods of 1992–1996 and 1996–2002. Based on the focal training sample, the model was calibrated using the cross-validation method, and the optimal bandwidth was determined. The results were compared with those of a nonlinear global SVM (GSVM) model that was calibrated based on the ten-fold cross-validation method. This study then evaluated an integrated SVM model (LGSVM) obtained based on a weighted combination of the local and global urban development probabilities. A comparison of the probability maps showed a higher accuracy for the LGSVM than for either the LSVM or GSVM model. To assess the performance of the LSVM, GSVM and LGSVM models in the simulation of urban growth, probability maps were employed as the transition rules for urban cellular automata. The results show that a trade-off between local and global SVM models can enhance the performance of urban growth modelling. View Full-Text
Keywords: support vector machines; cellular automata; probability maps; urban development support vector machines; cellular automata; probability maps; urban development

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Mirbagheri, B.; Alimohammadi, A. Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling. ISPRS Int. J. Geo-Inf. 2018, 7, 347.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top